X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_v4.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_v4.py;h=b4f07ea70edf69ecac94fad26fb949295a41eac0;hb=a785567fb9acfc68536767d20f60ba917ae85aa1;hp=0000000000000000000000000000000000000000;hpb=94a133e696b9b2a7f73544462c2714986fa7ab4a;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4.py new file mode 100755 index 0000000..b4f07ea --- /dev/null +++ b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v4.py @@ -0,0 +1,323 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains the definition of the Inception V4 architecture. + +As described in http://arxiv.org/abs/1602.07261. + + Inception-v4, Inception-ResNet and the Impact of Residual Connections + on Learning + Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from nets import inception_utils + +slim = tf.contrib.slim + + +def block_inception_a(inputs, scope=None, reuse=None): + """Builds Inception-A block for Inception v4 network.""" + # By default use stride=1 and SAME padding + with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], + stride=1, padding='SAME'): + with tf.variable_scope(scope, 'BlockInceptionA', [inputs], reuse=reuse): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(inputs, 96, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3') + branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 96, [1, 1], scope='Conv2d_0b_1x1') + return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + + +def block_reduction_a(inputs, scope=None, reuse=None): + """Builds Reduction-A block for Inception v4 network.""" + # By default use stride=1 and SAME padding + with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], + stride=1, padding='SAME'): + with tf.variable_scope(scope, 'BlockReductionA', [inputs], reuse=reuse): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(inputs, 384, [3, 3], stride=2, padding='VALID', + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3') + branch_1 = slim.conv2d(branch_1, 256, [3, 3], stride=2, + padding='VALID', scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID', + scope='MaxPool_1a_3x3') + return tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) + + +def block_inception_b(inputs, scope=None, reuse=None): + """Builds Inception-B block for Inception v4 network.""" + # By default use stride=1 and SAME padding + with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], + stride=1, padding='SAME'): + with tf.variable_scope(scope, 'BlockInceptionB', [inputs], reuse=reuse): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 224, [1, 7], scope='Conv2d_0b_1x7') + branch_1 = slim.conv2d(branch_1, 256, [7, 1], scope='Conv2d_0c_7x1') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1') + branch_2 = slim.conv2d(branch_2, 224, [1, 7], scope='Conv2d_0c_1x7') + branch_2 = slim.conv2d(branch_2, 224, [7, 1], scope='Conv2d_0d_7x1') + branch_2 = slim.conv2d(branch_2, 256, [1, 7], scope='Conv2d_0e_1x7') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1') + return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + + +def block_reduction_b(inputs, scope=None, reuse=None): + """Builds Reduction-B block for Inception v4 network.""" + # By default use stride=1 and SAME padding + with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], + stride=1, padding='SAME'): + with tf.variable_scope(scope, 'BlockReductionB', [inputs], reuse=reuse): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') + branch_0 = slim.conv2d(branch_0, 192, [3, 3], stride=2, + padding='VALID', scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 256, [1, 7], scope='Conv2d_0b_1x7') + branch_1 = slim.conv2d(branch_1, 320, [7, 1], scope='Conv2d_0c_7x1') + branch_1 = slim.conv2d(branch_1, 320, [3, 3], stride=2, + padding='VALID', scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID', + scope='MaxPool_1a_3x3') + return tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) + + +def block_inception_c(inputs, scope=None, reuse=None): + """Builds Inception-C block for Inception v4 network.""" + # By default use stride=1 and SAME padding + with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], + stride=1, padding='SAME'): + with tf.variable_scope(scope, 'BlockInceptionC', [inputs], reuse=reuse): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = tf.concat(axis=3, values=[ + slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'), + slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')]) + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 448, [3, 1], scope='Conv2d_0b_3x1') + branch_2 = slim.conv2d(branch_2, 512, [1, 3], scope='Conv2d_0c_1x3') + branch_2 = tf.concat(axis=3, values=[ + slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'), + slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')]) + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 256, [1, 1], scope='Conv2d_0b_1x1') + return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + + +def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None): + """Creates the Inception V4 network up to the given final endpoint. + + Args: + inputs: a 4-D tensor of size [batch_size, height, width, 3]. + final_endpoint: specifies the endpoint to construct the network up to. + It can be one of [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', + 'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', + 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', + 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c', + 'Mixed_7d'] + scope: Optional variable_scope. + + Returns: + logits: the logits outputs of the model. + end_points: the set of end_points from the inception model. + + Raises: + ValueError: if final_endpoint is not set to one of the predefined values, + """ + end_points = {} + + def add_and_check_final(name, net): + end_points[name] = net + return name == final_endpoint + + with tf.variable_scope(scope, 'InceptionV4', [inputs]): + with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], + stride=1, padding='SAME'): + # 299 x 299 x 3 + net = slim.conv2d(inputs, 32, [3, 3], stride=2, + padding='VALID', scope='Conv2d_1a_3x3') + if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points + # 149 x 149 x 32 + net = slim.conv2d(net, 32, [3, 3], padding='VALID', + scope='Conv2d_2a_3x3') + if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points + # 147 x 147 x 32 + net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3') + if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points + # 147 x 147 x 64 + with tf.variable_scope('Mixed_3a'): + with tf.variable_scope('Branch_0'): + branch_0 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', + scope='MaxPool_0a_3x3') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID', + scope='Conv2d_0a_3x3') + net = tf.concat(axis=3, values=[branch_0, branch_1]) + if add_and_check_final('Mixed_3a', net): return net, end_points + + # 73 x 73 x 160 + with tf.variable_scope('Mixed_4a'): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1') + branch_0 = slim.conv2d(branch_0, 96, [3, 3], padding='VALID', + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 64, [1, 7], scope='Conv2d_0b_1x7') + branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1') + branch_1 = slim.conv2d(branch_1, 96, [3, 3], padding='VALID', + scope='Conv2d_1a_3x3') + net = tf.concat(axis=3, values=[branch_0, branch_1]) + if add_and_check_final('Mixed_4a', net): return net, end_points + + # 71 x 71 x 192 + with tf.variable_scope('Mixed_5a'): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, 192, [3, 3], stride=2, padding='VALID', + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + branch_1 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', + scope='MaxPool_1a_3x3') + net = tf.concat(axis=3, values=[branch_0, branch_1]) + if add_and_check_final('Mixed_5a', net): return net, end_points + + # 35 x 35 x 384 + # 4 x Inception-A blocks + for idx in range(4): + block_scope = 'Mixed_5' + chr(ord('b') + idx) + net = block_inception_a(net, block_scope) + if add_and_check_final(block_scope, net): return net, end_points + + # 35 x 35 x 384 + # Reduction-A block + net = block_reduction_a(net, 'Mixed_6a') + if add_and_check_final('Mixed_6a', net): return net, end_points + + # 17 x 17 x 1024 + # 7 x Inception-B blocks + for idx in range(7): + block_scope = 'Mixed_6' + chr(ord('b') + idx) + net = block_inception_b(net, block_scope) + if add_and_check_final(block_scope, net): return net, end_points + + # 17 x 17 x 1024 + # Reduction-B block + net = block_reduction_b(net, 'Mixed_7a') + if add_and_check_final('Mixed_7a', net): return net, end_points + + # 8 x 8 x 1536 + # 3 x Inception-C blocks + for idx in range(3): + block_scope = 'Mixed_7' + chr(ord('b') + idx) + net = block_inception_c(net, block_scope) + if add_and_check_final(block_scope, net): return net, end_points + raise ValueError('Unknown final endpoint %s' % final_endpoint) + + +def inception_v4(inputs, num_classes=1001, is_training=True, + dropout_keep_prob=0.8, + reuse=None, + scope='InceptionV4', + create_aux_logits=True): + """Creates the Inception V4 model. + + Args: + inputs: a 4-D tensor of size [batch_size, height, width, 3]. + num_classes: number of predicted classes. + is_training: whether is training or not. + dropout_keep_prob: float, the fraction to keep before final layer. + reuse: whether or not the network and its variables should be reused. To be + able to reuse 'scope' must be given. + scope: Optional variable_scope. + create_aux_logits: Whether to include the auxiliary logits. + + Returns: + logits: the logits outputs of the model. + end_points: the set of end_points from the inception model. + """ + end_points = {} + with tf.variable_scope(scope, 'InceptionV4', [inputs], reuse=reuse) as scope: + with slim.arg_scope([slim.batch_norm, slim.dropout], + is_training=is_training): + net, end_points = inception_v4_base(inputs, scope=scope) + + with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], + stride=1, padding='SAME'): + # Auxiliary Head logits + if create_aux_logits: + with tf.variable_scope('AuxLogits'): + # 17 x 17 x 1024 + aux_logits = end_points['Mixed_6h'] + aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3, + padding='VALID', + scope='AvgPool_1a_5x5') + aux_logits = slim.conv2d(aux_logits, 128, [1, 1], + scope='Conv2d_1b_1x1') + aux_logits = slim.conv2d(aux_logits, 768, + aux_logits.get_shape()[1:3], + padding='VALID', scope='Conv2d_2a') + aux_logits = slim.flatten(aux_logits) + aux_logits = slim.fully_connected(aux_logits, num_classes, + activation_fn=None, + scope='Aux_logits') + end_points['AuxLogits'] = aux_logits + + # Final pooling and prediction + with tf.variable_scope('Logits'): + # 8 x 8 x 1536 + net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID', + scope='AvgPool_1a') + # 1 x 1 x 1536 + net = slim.dropout(net, dropout_keep_prob, scope='Dropout_1b') + net = slim.flatten(net, scope='PreLogitsFlatten') + end_points['PreLogitsFlatten'] = net + # 1536 + logits = slim.fully_connected(net, num_classes, activation_fn=None, + scope='Logits') + end_points['Logits'] = logits + end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions') + return logits, end_points +inception_v4.default_image_size = 299 + + +inception_v4_arg_scope = inception_utils.inception_arg_scope